Detecting Drowsiness Behind the Wheel: A Lightweight Approach Based on Eye and Mouth Aspect Ratios

Abstract

Driver distraction is a leading cause of traffic accidents, with fatigue being a significant contributor. This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine deep learning and 68 face model. The proposed system assesses driver tiredness by measuring the distance between key facial landmarks, such as the distance between the eyes and the angle of the mouth, to evaluate signs of drowsiness or disengagement. Real-time video feed analysis allows for continuous monitoring of the driver’s face, enabling the system to detect behavioral cues associated with distraction, such as eye closures or changes in facial expressions. The effectiveness of this method is demonstrated through a series of experiments on a dataset of driver videos, which proves that the approach can accurately assess tiredness and distraction levels under various driving conditions. By focusing on facial landmarks, the system is computationally efficient and capable of operating in real-time, making it a practical solution for in-vehicle safety systems. This paper discusses the system’s performance, limitations, and potential for future enhancements, including integration with other in-vehicle technologies to provide comprehensive driver monitoring.